{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "from sklearn.metrics import r2_score\n",
    "import copy\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "import shap\n",
    "from pathlib import Path\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn.metrics\n",
    "from matplotlib.pyplot import savefig  \n",
    "from matplotlib.pyplot import figaspect \n",
    "\n",
    "rand_nr = 42\n",
    "np.random.seed(rand_nr)\n",
    "\n",
    "master_folder = '/Users/christopherrauh/Dropbox/JEEA_replication/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def in_out(X, which, depo, futuro):\n",
    "\n",
    "\t\n",
    "\thurz = []\n",
    "\n",
    "\tfor ei in which:\n",
    "\t\thurz.append(ei)\n",
    "\thurz.append('isocode')\n",
    "\n",
    "\t#create variables and country list for prediction into the future \n",
    "\tfuture_ready = futuro[hurz]\n",
    "\tfuture_ready = future_ready.dropna(how='any')\n",
    "\tcountries_ready = future_ready[['isocode']]\n",
    "\tfuture_ready = future_ready.drop('isocode', axis=1)\n",
    "\n",
    "\t#create variables and outcomes to train model on past data \n",
    "\thurz = []\n",
    "\tfor ei in which:\n",
    "\t\thurz.append(ei)\n",
    "\thurz.append(depo)\n",
    "\tX = X[hurz]\n",
    "\tX = X.dropna(how='any')\t\t\t\t\t\t\t\n",
    "\tdf2 = X[[depo]]\n",
    "\tX = X[which]\n",
    "\ty = df2.values.ravel()\n",
    "\n",
    "\n",
    "\n",
    "\treturn X, y, future_ready, countries_ready\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tops = 30\n",
    "dup = 'anyviolence'\n",
    "g = 1\n",
    "q = 0\n",
    "\n",
    "inputter = []\n",
    "\n",
    "dummies = []\n",
    "\n",
    "text = []\n",
    "\n",
    "\n",
    "for z in range(0,tops):\n",
    "    this = 'ste_theta' + str(z)\n",
    "    text.append(this)\n",
    "    this = 'ste_theta' + str(z) + '_stock'\n",
    "    text.append(this)\n",
    "\n",
    "this = 'tokens'\n",
    "text.append(this)\n",
    "\n",
    "dummies = ['anyviolence_dp', 'armedconf_dp', 'civilwar_dp']\n",
    "if dup != 'anyviolence':\n",
    "    dummies.append('best')\n",
    "mine = text + dummies\n",
    "inputter.append(mine)\n",
    "inputter.append(text)\n",
    "inputter.append(dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, min_samples_leaf=6, min_samples_split=6,\n",
       "                       n_estimators=400, random_state=2520)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#estimate model\n",
    "depth = 8\n",
    "dep = 'ons_' + dup + str(g)\n",
    "my_file = Path(master_folder + 'merged_1_3_topics30_200_m8_y2020.csv')\n",
    "data = pd.read_csv(my_file, sep=',')\n",
    "future = data\n",
    "[X, Y, future_out, countries] = in_out(data, inputter[q], dep, future)\n",
    "\n",
    "#clf = RandomForestClassifier(max_depth=depth,n_estimators =700,min_samples_split =18,min_samples_leaf=3, random_state=2520)\n",
    "clf = RandomForestClassifier(max_depth=depth,n_estimators =400,min_samples_split =6,min_samples_leaf=6, random_state=2520)\n",
    "clf.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#topic_names = ['Economy', 'International relations', 'Media', 'Industry','Sanctions','Terror','Policy','Diplomacy','Health','Military','Daily life','Police','Administration','Politics','Internet']\n",
    "topic_names = ['Justice','Middle East','Economics','Current events','Industry','Nuclear treaties','Military','Urban','Intelligence','Russia','Transport','Peace agreements','Daily life'\n",
    "               ,'State visits','Health','International relations','Politics','Religion','Middle East II','Activism','Finance','Parliament','China'\n",
    "              ,'South America','Terror','Media','Trade','Political power','India-Pakistan','Borders']\n",
    "names = []\n",
    "for to in topic_names:\n",
    "    names.append(to+' topic share')\n",
    "    names.append(to+' topic stock')\n",
    "    \n",
    "other = ['Tokens', 'Months since last battle death', 'Months since 50 battle deaths' , 'Months since 500 battle deaths']\n",
    "for to in other:\n",
    "    names.append(to)\n",
    "###next\n",
    "\n",
    "preds_name = inputter[q]\n",
    "\n",
    "\n",
    "importances = list(clf.feature_importances_)# List of tuples with variable and importance\n",
    "feature_importances = [(feature, importance) for feature, importance in zip(names, importances)]# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = False)# Print out the feature and importances \n",
    "\n",
    "\n",
    "x_axis = []\n",
    "y_axis = []\n",
    "y_axis2 = []\n",
    "error = []\n",
    "for pair in feature_importances:\n",
    "    x_axis.append(pair[0])\n",
    "    y_axis.append(pair[1])\n",
    "    #y_axis2.append(pair[2])\n",
    "    #error.append(pair[3])\n",
    "\n",
    "\n",
    "x_pos = [i*2 for i, _ in enumerate(x_axis)]\n",
    "\n",
    "\n",
    "\n",
    "plt.axes([0, 0, 0.75, 2])\n",
    "plt.barh(x_pos, y_axis, color='red',height=1,label='Impurity importance')\n",
    "\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"\")\n",
    "plt.title(\"\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.yticks(x_pos,x_axis)\n",
    "#Figure \n",
    "plt.savefig('Figures_superall/feature_importance_both.pdf',  bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of nodes 257\n",
      "Average maximum depth 8\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n_nodes = []\n",
    "max_depths = []\n",
    "children_left = []\n",
    "children_right = []\n",
    "feature = []\n",
    "threshold = []\n",
    "\n",
    "for ind_tree in clf.estimators_:\n",
    "    n_nodes.append(ind_tree.tree_.node_count)\n",
    "    max_depths.append(ind_tree.tree_.max_depth)\n",
    "    children_left.append(ind_tree.tree_.children_left)\n",
    "    children_right.append(ind_tree.tree_.children_right)\n",
    "    feature.append(ind_tree.tree_.feature)\n",
    "print(f'Average number of nodes {int(np.mean(n_nodes))}')\n",
    "print(f'Average maximum depth {int(np.mean(max_depths))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{61: [1, 5, 3, 7, 8, 5, 5],\n",
       " 48: [2, 7, 8],\n",
       " 34: [3, 5, 7],\n",
       " 33: [4, 8, 7],\n",
       " 60: [6],\n",
       " 56: [7, 7, 7],\n",
       " 16: [8, 7],\n",
       " 55: [7, 8, 7, 8],\n",
       " 62: [8, 7, 8, 3, 8, 7],\n",
       " 46: [6],\n",
       " 44: [7],\n",
       " 57: [8, 8, 6],\n",
       " 30: [8, 8],\n",
       " 17: [4, 6, 8, 6, 7],\n",
       " 42: [6],\n",
       " 29: [8, 8],\n",
       " 19: [8, 7, 8, 5, 8],\n",
       " 37: [6, 4],\n",
       " 36: [7, 6, 8],\n",
       " 13: [5, 8, 7, 8, 6],\n",
       " 45: [7, 6, 3, 6],\n",
       " 21: [8],\n",
       " 2: [8, 7, 8],\n",
       " 8: [5, 8, 7],\n",
       " 49: [6, 7, 6],\n",
       " 7: [8],\n",
       " 18: [8, 6],\n",
       " 43: [7],\n",
       " 53: [8, 6],\n",
       " 9: [7],\n",
       " 35: [8, 4],\n",
       " 63: [7, 4, 5, 4],\n",
       " 0: [5, 6],\n",
       " 39: [6, 8],\n",
       " 5: [7, 6, 7],\n",
       " 51: [6, 8],\n",
       " 15: [8, 7],\n",
       " 41: [8],\n",
       " 12: [2, 8],\n",
       " 54: [8, 6],\n",
       " 4: [7],\n",
       " 38: [7],\n",
       " 28: [8],\n",
       " 26: [8, 8],\n",
       " 24: [8, 6],\n",
       " 59: [7],\n",
       " 32: [5, 7, 8, 7],\n",
       " 1: [4, 5],\n",
       " 11: [8],\n",
       " 6: [7, 5],\n",
       " 3: [8],\n",
       " 40: [8],\n",
       " 27: [8],\n",
       " 10: [8],\n",
       " 23: [7]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how to interpret features\n",
    "#https://stackoverflow.com/questions/39708304/clf-tree-feature-what-is-the-output-scikit-learn\n",
    "positions = []\n",
    "positions_single = dict()\n",
    "for j in feature:  \n",
    "    posis = dict()\n",
    "    #j = feature[0]\n",
    "    pos = 0\n",
    "    last = 0\n",
    "    still_open = []\n",
    "    for i in j:\n",
    "\n",
    "        #-2 means move up\n",
    "        if i == -2:\n",
    "            if last != -2:\n",
    "                still_open = still_open[:-1]\n",
    "            if last == -2:\n",
    "                #go to last open one\n",
    "                if still_open != []:\n",
    "                    pos = still_open[-1]\n",
    "                    #and then delete it\n",
    "                    still_open = still_open[:-1]\n",
    "        else:\n",
    "            pos = pos + 1\n",
    "\n",
    "            still_open.append(pos)\n",
    "            if i in posis:\n",
    "                posis[i].append(pos)\n",
    "                \n",
    "            else:\n",
    "                posis[i] = [pos]\n",
    "            if i in positions_single:\n",
    "                positions_single[i].append(pos)\n",
    "            else: \n",
    "                positions_single[i] = [pos]\n",
    "\n",
    "        last = i + 0\n",
    "    positions.append(posis)\n",
    "\n",
    "positions[1]\n",
    "#count how often at top position\n",
    "#compute average position\n",
    "#distribution of position\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{131: -1, 1: 1, 8: 255, 4: 33, 5: 76, 6: 122, 7: 167, 3: 16, 2: 7},\n",
       " {137: -1, 2: 10, 3: 38, 8: 298, 6: 168, 4: 52, 5: 91, 7: 211, 1: 2},\n",
       " {123: -1, 4: 78, 5: 95, 7: 253, 8: 320, 3: 49, 1: 28, 2: 35, 6: 172},\n",
       " {113: -1, 5: 98, 8: 255, 7: 186, 6: 141, 3: 43, 4: 56, 1: 9, 2: 14},\n",
       " {129: -1, 6: 120, 7: 193, 8: 250, 4: 24, 5: 70, 3: 10},\n",
       " {162: -1, 7: 508, 6: 421, 8: 570, 4: 238, 5: 299, 3: 148, 2: 87, 1: 51},\n",
       " {117: -1, 8: 258, 6: 119, 4: 61, 7: 175, 5: 83, 2: 11, 3: 23, 1: 1},\n",
       " {149: -1, 8: 374, 4: 133, 7: 264, 6: 259, 5: 199, 3: 90, 2: 67, 1: 42},\n",
       " {161: -1, 4: 278, 7: 663, 6: 568, 3: 194, 8: 809, 5: 421, 1: 45, 2: 108},\n",
       " {139: -1, 5: 80, 8: 253, 6: 123, 2: 16, 7: 206, 3: 21, 4: 49, 1: 14},\n",
       " {102: -1, 7: 175, 8: 264, 3: 15, 5: 61, 4: 33, 6: 118, 2: 4},\n",
       " {135: -1, 8: 356, 6: 199, 4: 115, 7: 273, 5: 124, 1: 13, 2: 26, 3: 52},\n",
       " {141: -1, 8: 260, 7: 187, 4: 35, 6: 105, 3: 19, 5: 64, 2: 8, 1: 4},\n",
       " {148: -1, 8: 267, 6: 174, 7: 194, 2: 48, 1: 32, 5: 108, 4: 85, 3: 71},\n",
       " {154: -1, 6: 117, 7: 189, 8: 258, 4: 27, 5: 45, 3: 8, 2: 7, 1: 1},\n",
       " {136: -1, 7: 203, 8: 296, 6: 124, 5: 60, 4: 24, 2: 3, 3: 11},\n",
       " {160: -1, 8: 323, 5: 179, 7: 279, 6: 196, 3: 66, 4: 129, 2: 37, 1: 16},\n",
       " {111: -1, 8: 252, 6: 137, 7: 211, 5: 80, 4: 55, 2: 3, 3: 24},\n",
       " {150: -1, 8: 257, 7: 201, 6: 108, 5: 49, 4: 29, 3: 3},\n",
       " {163: -1, 8: 470, 4: 153, 2: 88, 7: 345, 5: 209, 6: 265, 1: 33, 3: 121},\n",
       " {145: -1, 7: 238, 6: 138, 3: 39, 8: 282, 5: 86, 1: 19, 2: 25, 4: 53},\n",
       " {105: -1, 8: 242, 7: 183, 6: 136, 5: 55, 4: 30, 3: 5, 2: 1},\n",
       " {116: -1, 8: 279, 6: 107, 7: 168, 1: 1, 5: 51, 4: 27, 3: 9, 2: 8},\n",
       " {157: -1, 8: 350, 5: 134, 6: 173, 7: 233, 2: 25, 4: 69, 3: 44, 1: 14},\n",
       " {119: -1, 8: 284, 6: 174, 7: 225, 5: 99, 4: 38, 3: 22, 2: 5},\n",
       " {144: -1, 7: 168, 8: 267, 6: 104, 4: 24, 5: 59, 3: 6, 2: 2, 1: 3},\n",
       " {100: -1, 8: 230, 7: 174, 5: 56, 6: 87, 3: 6, 4: 21},\n",
       " {152: -1, 5: 41, 7: 164, 8: 247, 6: 99, 4: 18, 3: 5, 2: 2},\n",
       " {146: -1, 6: 121, 8: 283, 4: 39, 7: 198, 5: 49, 3: 7},\n",
       " {118: -1, 7: 200, 8: 244, 6: 138, 5: 67, 2: 1, 4: 18, 3: 5},\n",
       " {159: -1, 8: 275, 5: 86, 7: 206, 6: 150, 3: 9, 4: 28, 2: 6},\n",
       " {107: -1, 7: 170, 6: 91, 8: 251, 5: 70, 4: 25, 2: 3, 3: 13},\n",
       " {124: -1, 8: 264, 6: 114, 7: 174, 5: 56, 4: 15, 3: 4, 2: 2},\n",
       " {140: -1, 5: 70, 8: 268, 7: 157, 6: 106, 4: 34, 3: 11, 2: 3},\n",
       " {104: -1, 7: 159, 6: 107, 8: 254, 5: 69, 4: 21, 3: 3, 2: 1},\n",
       " {127: -1, 8: 230, 6: 116, 4: 30, 7: 166, 5: 70, 2: 4, 3: 10},\n",
       " {108: -1, 7: 215, 5: 79, 8: 267, 6: 113, 3: 9, 4: 24, 2: 2},\n",
       " {120: -1, 8: 245, 7: 178, 5: 55, 4: 14, 6: 105, 3: 3, 2: 2},\n",
       " {103: -1, 7: 234, 8: 303, 3: 36, 4: 55, 2: 22, 1: 19, 5: 97, 6: 134},\n",
       " {142: -1, 7: 161, 6: 90, 8: 242, 5: 55, 4: 23, 3: 3},\n",
       " {112: -1, 6: 115, 5: 57, 8: 264, 2: 8, 4: 34, 7: 176, 3: 19, 1: 6},\n",
       " {125: -1, 3: 7, 6: 111, 7: 173, 8: 253, 4: 29, 5: 60, 2: 2},\n",
       " {138: -1, 5: 49, 6: 111, 7: 199, 8: 238, 4: 15, 3: 8, 2: 2, 1: 4},\n",
       " {106: -1, 7: 165, 8: 205, 5: 53, 4: 19, 6: 97, 3: 12, 2: 4},\n",
       " {158: -1, 8: 263, 7: 177, 5: 56, 4: 28, 6: 122, 3: 11, 2: 2},\n",
       " {153: -1, 8: 310, 6: 162, 7: 186, 5: 82, 4: 50, 2: 16, 3: 18, 1: 8},\n",
       " {126: -1, 6: 85, 7: 159, 8: 219, 5: 51, 4: 15, 3: 6},\n",
       " {130: -1, 8: 236, 6: 93, 7: 150, 3: 9, 5: 68, 4: 20},\n",
       " {128: -1, 8: 254, 7: 163, 6: 111, 5: 52, 4: 21, 2: 2, 3: 5},\n",
       " {133: -1, 8: 201, 4: 20, 7: 134, 6: 103, 5: 49, 3: 12, 2: 5},\n",
       " {132: -1, 7: 162, 8: 225, 5: 43, 6: 90, 4: 27, 3: 4, 2: 2},\n",
       " {122: -1, 8: 310, 7: 220, 5: 97, 4: 72, 6: 118, 3: 43, 1: 19, 2: 23},\n",
       " {156: -1, 4: 47, 5: 73, 7: 199, 8: 257, 6: 125, 2: 11, 3: 28, 1: 4},\n",
       " {110: -1, 6: 100, 8: 254, 7: 187, 4: 26, 3: 12, 5: 70},\n",
       " {143: -1, 8: 230, 7: 181, 6: 104, 5: 51, 4: 28, 3: 7, 2: 2},\n",
       " {121: -1, 6: 103, 8: 229, 5: 57, 4: 36, 7: 164, 2: 3, 3: 26, 1: 5},\n",
       " {134: -1, 3: 31, 5: 100, 7: 207, 6: 135, 4: 56, 8: 271, 2: 10, 1: 3},\n",
       " {155: -1, 7: 234, 8: 265, 5: 63, 1: 3, 6: 135, 4: 39, 3: 12, 2: 4},\n",
       " {109: -1, 7: 186, 3: 11, 6: 124, 8: 279, 4: 26, 5: 59, 2: 1},\n",
       " {151: -1, 6: 117, 8: 215, 5: 65, 7: 163, 4: 17, 3: 4},\n",
       " {115: -1, 8: 226, 7: 190, 5: 67, 6: 90, 4: 27, 3: 4, 2: 2},\n",
       " {101: -1, 4: 37, 5: 62, 8: 240, 7: 167, 6: 104, 3: 15, 2: 4},\n",
       " {114: -1, 6: 92, 8: 241, 5: 40, 7: 145, 4: 18, 3: 4, 2: 2},\n",
       " {147: -1, 4: 47, 7: 224, 6: 158, 5: 87, 8: 294, 3: 12, 2: 2}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_name = inputter[q]\n",
    "counts = []\n",
    "\n",
    "for key in positions_single:\n",
    "    counts2 = dict()\n",
    "    counts2[key+100] = -1\n",
    "\n",
    "    for pos in positions_single[key]:\n",
    "        if pos not in counts2:\n",
    "            counts2[pos] = 1\n",
    "        else:\n",
    "            counts2[pos] = 1 + counts2[pos]\n",
    "    counts.append(counts2)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{8: 284, 6: 174, 7: 225, 5: 99, 4: 38, 3: 22, 2: 5}\n",
      "   column1   19\n",
      "6        2    5\n",
      "5        3   22\n",
      "4        4   38\n",
      "3        5   99\n",
      "1        6  174\n",
      "2        7  225\n",
      "0        8  284\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "    \n",
    "dfx = None\n",
    "\n",
    "for hei in counts:\n",
    "    hurz = copy.copy(hei)\n",
    "    for key in hurz:\n",
    "        if key >= 100:\n",
    "            who = key - 100    \n",
    "    \n",
    "    del hurz[who+100]\n",
    "    \n",
    "    df = pd.DataFrame(list(hurz.items()),columns = ['column1',who])\n",
    "    \n",
    "    df = df.sort_values('column1')\n",
    "    \n",
    "    if who == 19:\n",
    "        print(hurz)\n",
    "        print(df)\n",
    "    df = df.reset_index()\n",
    "    #df.index += 1 \n",
    "    df.index = df['column1']\n",
    "    df1_transposed = df.T\n",
    "\n",
    "    df1_transposed = df1_transposed.drop('index')\n",
    "    df1_transposed = df1_transposed.drop('column1')\n",
    "    \n",
    "    if dfx is None:\n",
    "        dfx = df1_transposed\n",
    "    else:\n",
    "        dfx = dfx.append(df1_transposed)\n",
    "dfx = dfx.fillna(0)\n",
    "dfx.sort_index(inplace=True) \n",
    "\n",
    "sns.set(font_scale=0.3)\n",
    "huch = copy.copy(dfx)\n",
    "huch = np.log(dfx + 1)\n",
    "huch = huch.replace([np.inf, -np.inf], np.nan)\n",
    "ax = sns.heatmap(huch, yticklabels=names, cmap='Reds',cbar_kws={'label': 'Log(Frequency + 1)'})\n",
    "#ax.set_facecolor(\"black\")\n",
    "plt.xlabel('Depth')\n",
    "#Figure C3\n",
    "plt.savefig(\"Figures_superall/where_in_tree.pdf\", format='pdf', bbox_inches = \"tight\")\n",
    "plt.close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#do one where each column sums to one\n",
    "for v in range(1,depth+1):\n",
    "  \n",
    "    dfx[v] = dfx[v]/dfx[v].mean()\n",
    "    dfx[v] = np.log(dfx[v])\n",
    "dfx = dfx.replace([np.inf, -np.inf], np.nan)\n",
    "ax = sns.heatmap(dfx, yticklabels=names,cmap = 'bwr',vmin=-2,vmax=2,cbar_kws={'label': 'Log(frequency/mean frequency)'})\n",
    "plt.xlabel('Depth')\n",
    "#colormap.set_bad(\"black\") \n",
    "ax.set_facecolor(\"black\")\n",
    "#Figure C4\n",
    "plt.savefig(\"Figures_superall/rel_where_in_tree.pdf\", format='pdf', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x626.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(X, Y)\n",
    "explainer = shap.TreeExplainer(clf)\n",
    "shap_values = explainer.shap_values(X)\n",
    "shap.summary_plot(shap_values[1], features=X, feature_names=names,max_display=18, show=False,cmap=\"copper\")\n",
    "#Figure 7\n",
    "plt.savefig('Figures_superall/shap_importance_gray.pdf',  bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_name = inputter[q]\n",
    "for v in range(0,len(preds_name)):\n",
    "\n",
    "    #shap.dependence_plot(v, shap_values, X.values, feature_names=preds_names, interaction_index='High SES', show=False)\n",
    "    shap.dependence_plot(v, shap_values[1], X, feature_names=names, interaction_index='Months since last battle death', show=False)\n",
    "    plt.ylabel(\"\")\n",
    "    #Figure C5\n",
    "    plt.savefig('Figures_superall/shap_importance'+str(v)+'.pdf',  bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  57968  observations\n"
     ]
    }
   ],
   "source": [
    "#drawing trees\n",
    "\n",
    "#making tree\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn import tree\n",
    "import pydot\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import savefig\n",
    "from treeinterpreter import treeinterpreter as ti\n",
    "\n",
    "#preds_name\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "N = len(Y)\n",
    "\n",
    "print('There are ', len(Y), ' observations')\n",
    "\n",
    "\n",
    "for t in [3,8]:\n",
    "\n",
    "\n",
    "    model = RandomForestClassifier(max_depth=t,n_estimators =1,min_samples_split =100,max_features='auto',min_samples_leaf=100, random_state=2520,bootstrap=False)\n",
    "    if t == 8:\n",
    "        model = RandomForestClassifier(max_depth=t,n_estimators =1,min_samples_split =6,max_features='auto',min_samples_leaf=6, random_state=2520,bootstrap=False)\n",
    "\n",
    "\n",
    "    model.fit(X, Y);\n",
    "\n",
    "\n",
    "    for i_tree in [0]:\n",
    "        tree =  model.estimators_[i_tree]\n",
    "        # Export the image to a dot file\n",
    "        dotfile = 'tree_' + str(i_tree) + '.dot'\n",
    "        \n",
    "        export_graphviz(tree, out_file = dotfile, feature_names = names, rounded = True, impurity = False, filled = True)\n",
    "\n",
    "        # Use dot file to create a graph\n",
    "        (graph, ) = pydot.graph_from_dot_file(dotfile)\n",
    "\n",
    "        colors = ('turquoise', 'orange')\n",
    "        edges = collections.defaultdict(list)\n",
    "\n",
    "        for edge in graph.get_edge_list():\n",
    "            edges[edge.get_source()].append(int(edge.get_destination()))\n",
    "\n",
    "        for edge in edges:\n",
    "            edges[edge].sort()    \n",
    "            for i in range(2):\n",
    "                dest = graph.get_node(str(edges[edge][i]))[0]\n",
    "                dest.set_fillcolor(colors[i])\n",
    "\n",
    "        # Write graph to a png file\n",
    "        #Figure C1\n",
    "        graph.write_png('tree' + str(i_tree) +  '_trees' + str(t) +  '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['isocode', 'text', 'dummies', 'mine', 'dep', 'year', 'month', 'best',\n",
      "       'anyviolence', 'anyviolence_dp', 'armedconf_dp', 'civilwar_dp',\n",
      "       'ons_anyviolence1', 'ons_anyviolence3', 'ons_anyviolence12',\n",
      "       'ons_armedconf1', 'ons_armedconf3', 'ons_armedconf12', 'ons_civilwar1',\n",
      "       'ons_civilwar3', 'ons_civilwar12', 'region_o', 'subregion_o'],\n",
      "      dtype='object')\n",
      "0.0298324895518331\n",
      "29431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0024464000543644457\n",
      "29431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001677500524218914\n",
      "19076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
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      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0006066243377684312\n",
      "24727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n",
      "invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "#Matthews correlation\n",
    "\n",
    "my_file = master_folder + 'RandomForest1_3_200_0_superall_topics30_0_1.csv'\n",
    "data = pd.read_table(my_file, sep=',')\n",
    "print(data.columns)\n",
    "\n",
    "                        \n",
    "outs = []\n",
    "step_out = []\n",
    "\n",
    "for v in [0,1]:\n",
    "    if v == 0:\n",
    "        step = np.linspace(0,0.8,100)\n",
    "    if v == 1:\n",
    "        step = np.linspace(0,0.2,100)\n",
    "    for d in ['anyviolence','armedconf']:    \n",
    "        dup = 'ons_' + d + '1'\n",
    "    \n",
    "        datax = data[['dummies','ons_anyviolence1','ons_armedconf1','text','mine','dep','anyviolence_dp','armedconf_dp']]\n",
    "        \n",
    "        datay = datax.drop(datax[(datax['dep'] != dup)].index)\n",
    "        datay = datay.dropna(how='any')\n",
    "        if v == 1:\n",
    "            shortdep = d + '_dp'\n",
    "            datay = datay.drop(datay[(datay[shortdep] <= 120)].index)\n",
    "        print(datay[dup].mean())\n",
    "        print(len(datay))\n",
    "        for w in ['text','dummies','mine']:\n",
    "            corrs = []\n",
    "            steps = []\n",
    "            for c in step:\n",
    "                datay['hurz'] = datay[w].apply(lambda x: 1 if x > c else 0)\n",
    "                corr = sklearn.metrics.matthews_corrcoef(datay[dup],datay['hurz']) \n",
    "                corrs.append(corr)\n",
    "                steps.append(c)\n",
    "                #datay.drop(['hurz'], axis=1)\n",
    "            outs.append(corrs)\n",
    "            step_out.append(steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axnum = 0\n",
    "\n",
    "mytitle = ['Any violence (all cases)','Any violence (hard cases)']\n",
    "myx = ['Cutoff','Cutoff']\n",
    "myy = ['Matthews correlation coefficient', '']\n",
    "labels = ['Text', 'History', 'Text & history'] \n",
    "colors = ['darkgreen', 'darkorange', 'navy', 'red']\n",
    "lines = [':', '--', '-',  '-.']\n",
    "\n",
    "\n",
    "SMALL_SIZE = 6\n",
    "MEDIUM_SIZE = 8\n",
    "BIGGER_SIZE = 10\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "\n",
    "f = 0\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "\n",
    "for p in range(0,2):\n",
    "    for d in range(0,1):\n",
    "        axnum = axnum + 1\n",
    "\n",
    "        plt.subplot(1,2,axnum)\n",
    "\n",
    "        for i in range(0,len(labels)):\n",
    "            plt.plot(step_out[f], outs[f],label=labels[i],color=colors[i], linestyle=lines[i], linewidth=2)\n",
    "\n",
    "            f = f + 1\n",
    "        \n",
    "        plt.ylabel(myy[axnum-1])\n",
    "        plt.xlabel(myx[axnum-1])\n",
    "        plt.legend(frameon=False,fancybox=True, framealpha=0.5,loc='upper right')\n",
    "        plt.title(mytitle[axnum-1])\n",
    "        f = f + 3\n",
    "fig.tight_layout()\n",
    "\n",
    "figaspect(1.0)\n",
    "#Figure E3\n",
    "savefig(master_folder+'Figures_superall/matthew_correlation.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
